Protein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task.
A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations.
For more details, please refer to the benchmark test data set (bionlp-st-ge-2016-test).

The PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short).

Coreference annotation to the benchmark data set (reference and test) of BioNLP-ST 2016 GE task.
For detailed information, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).

The training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013.
The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component.

228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. For more info, please see Groza et al. "Automatic concept recognition using the human phenotype ontology reference and test suite corpora", 2015.

In order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations. In order to support the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences, we publish CoMAGC, a corpus with multi- faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, the corpus deals with changes in gene expression levels among other types of gene changes.

It is the benchmark reference data set of the BioNLP-ST 2016 GE task.
It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins.
The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible.
For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test).
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set (this project)
bionlp-st-ge-2016-test: benchmark test data set (annotations are blined)
bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set
Following is supporting resources:
bionlp-st-ge-2016-coref: coreference annotation
bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs.
pmc-enju-pas: dependency parsing result produced by Enju
UBERON-AE: annotation for anatomical entities as defined in UBERON
ICD10: annotation for disease names as defined in ICD10
GO-BP: annotation for biological process names as defined in GO
GO-CC: annotation for cellular component names as defined in GO
A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.

Cyanobacteria are prokaryotic organisms that have served as important model organisms for studying oxygenic photosynthesis and have played a significant role in the Earthfs history as primary producers of atmospheric oxygen.
Publication: http://www.aclweb.org/anthology/W12-2430

1.1 K

Kazusa DNA Research Institute and Database Center for Life Science (DBCLS)

It is the benchmark test data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 14 full paper articles which are about NFκB proteins. For testing purpose, however, annotations are all blinded, which means users cannot see the annotations in this project. Instead, annotations in any other project can be compared to the hidden annotations in this project, then the annotations in the project will be automatically evaluated based on the comparison.
A participant of GE task can get the evaluation of his/her result of automatic annotation, through following process:
Create a new project.
Import documents from the project, bionlp-st-2016-test-proteins to your project.
Import annotations from the project, bionlp-st-2016-test-proteins to your project.
At this point, you may want to compare you project to this project, the benchmark data set. It will show that protein annotations in your project is 100% correct, but other annotations, e.g., events, are 0%.
Produce event annotations, using your system, upon the protein annotations.
Upload your event annotations to your project.
Compare your project to this project, to get evaluation.
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set
bionlp-st-ge-2016-test: benchmark test data set (this project)
bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set
Following is supporting resources:
bionlp-st-ge-2016-coref: coreference annotation
bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs.
pmc-enju-pas: dependency parsing result produced by Enju
UBERON-AE: annotation for anatomical entities as defined in UBERON
ICD10: annotation for disease names as defined in ICD10
GO-BP: annotation for biological process names as defined in GO
GO-CC: annotation for cellular component names as defined in GO
A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.